Improving Speech Recognition and Understanding using Error-Corrective Reranking
Minwoo Jeong, Gary Geunbae Lee · ACM Transactions on Asian Language Information Processing · 2008
The main issues of practical spoken-language applications for human-computer interface are how to overcome speech recognition errors and guarantee the reasonable end-performance of spoken-language applications. Therefore, handling the erroneously recognized outputs is a key in developing robust spoken-language systems. To address this problem, we present a method to improve the accuracy of speech recognition and performance of spoken-language applications. The proposed error corrective reranking approach exploits recognition environment characteristics and domain-specific semantic information to provide robustness and adaptability for a spoken-language system. We demonstrate some experiments of spoken dialogue tasks and empirical results that show an improvement in accuracy for both speech recognition and spoken-language understanding. In our experiment, we show an error reduction of up to 9.7% and 16.8%; of word error rate, and 5.5% and 7.9% of understanding error for the air travel and telebanking service domains.